Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 25 appraisals
Journal of medical Internet research
Barriers and Facilitators to Implementing Digital Health Technologies for Remote Management of NCDs in Rural Areas: Mixed Methods Systematic Review
BACKGROUND: Digital health technologies (DHTs) have the potential to improve care delivery and outcomes for patients with noncommunicable diseases. Yet their implementation in rural settings remains uneven, and the factors influencing uptake are not well understood. OBJECTIVE: This mixed methods systematic review aimed to identify barriers and facilitators influencing the implementation and use of DHTs for remote management of noncommunicable diseases in rural areas. METHODS: We searched Medline, Embase, and CINAHL from inception to February 12, 2026, using terms related to digital health, noncommunicable diseases, and rural settings. Following the Joanna Briggs Institute methodology for mixed-method systematic review, we synthesized quantitative and qualitative studies. Barriers and facilitators were categorized using the Consolidated Framework for Implementation Research, and study quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: From the initial 1491 records, 14 studies met the inclusion criteria, with most conducted in high-income countries (n=11). Key barriers included technical challenges (software instability and hardware issues), poor internet connectivity, financial constraints, and workforce constraints, such as staff shortages and heavy workloads. Key facilitators included user-friendly technology design, strong leadership, effective teamwork, and ongoing communication. Evidence was predominantly qualitative, with only limited quantitative data available. CONCLUSIONS: DHTs show promise for improving access and continuity of care for cardiovascular disease, hypertension, and diabetes in rural settings; however, their impact is constrained by structural inequities, including limited broadband access, workforce shortages, and financial fragility. These findings highlight important implications for research, policy, and practice, including the need for rigorous mixed methods evaluations sensitive to rural contexts, long-term equity-oriented financing mechanisms, and strengthened organizational readiness to support effective DHT uptake.
2 Aug 2026
Read appraisal →BMJ open ophthalmology
Screening for diabetic retinopathy with artificial intelligence in a primary care setting: a comparative cost analysis
OBJECTIVE: Cost analysis of autonomous artificial intelligence (AI)-based screening of diabetic retinopathy (DR) for adults with diabetes at a primary care clinic. METHODS AND ANALYSIS: This study provides a comparative cost analysis of actual results using AI-based DR screening with counterfactual results based on all patients going through the physician-based referral system. A cost analysis is conducted using cost data from published sources, provincial billing codes, statistical sources, and patient characteristics from a clinical study to compare autonomous AI-based screening for DR versus physician-based screening. Costs considered include direct costs of operating the AI system, physician fees, and indirect costs to patient time. Along with total cost comparisons, a cost per DR case detected is estimated and a sensitivity analysis based on variations in AI costs is provided. RESULTS: Over the study period, 202 participants were screened for DR using autonomous AI. The majority (93.6%, n=189) of AI-based DR screening exams were completed successfully. The AI-based scenario results in total direct costs of $C7919.04 and indirect costs of $C5728.80, resulting in total costs of $C13 647.84 per 100 patients. The traditional physician-based approach results in total direct costs of $C8240 and indirect costs of $C19 998.09, resulting in total costs of $C28 238.09 for 100 patients. When costs are converted to costs per unit outcome, the total cost per diagnosed DR case is $C620.36 for the AI-based approach and $C1283.55 for the physician-based approach; the AI-based cost per diagnosed case was 52% lower. CONCLUSION: Given the lower cost per diagnosed case of the AI-based approach, there are advantages to the implementation of AI-based screening for DR.
1 Aug 2026
Read appraisal →ACS applied materials & interfaces
Advances and Challenges in Wearable Sensors for Health Monitoring
Analytical tools may revolutionize healthcare by enabling accessible, rapid, and decentralized testing. Wearable (bio)sensors, in particular, provide frequent or continuous patient monitoring through non- to minimally invasive measurements. This approach yields unprecedented amounts of health-related information, leading to more informed clinical decision-making and closer patient follow-up. In this mega-review article, we bring together leading researchers in the field to discuss the state of the art in wearable devices for health monitoring. We begin by providing a broad overview of the field through citation network analysis. We then review the application of chemical (bio)sensors in biofluids (e.g., sweat, saliva, tears, interstitial fluid, and cerebrospinal fluid), highlighting the challenges and advantages associated with each. Subsequently, we discuss the construction of wearable devices and their main formats (e.g., smart contact lenses, textiles, mouthguards, watches/wristbands, and implantable systems). Physical sensors are addressed in a dedicated section focusing on the assessment of heart rate, blood pressure, and body temperature. The role of soft electronics in wearable devices is also examined, as these technologies are essential for enhancing user comfort and sensor reliability, which demands advances in materials science. Furthermore, we present strategies for signal acquisition and transmission, as well as approaches for on-body energy harvesting and device self-powering. The use of artificial intelligence and machine learning is then discussed as a means of enhancing analytical performance and managing the large volumes of data generated by wearable devices. Finally, business, regulatory, and ethical considerations are examined. We expect that this review will provide an overview of sensing and biosensing technologies for health-related applications, identify promising research directions, and inspire future developments.
30 July 2026
Read appraisal →Analytical chemistry
Wearable and Multimodal Electrochemical Hydrogel Sensor for Real-Time Non-Invasive Sweat Glucose Monitoring
Noninvasive sweat glucose monitoring is a promising strategy for real-time health management. In this study, we developed a flexible electrochemical sensor platform based on gold nanorods@polylysine-glucose oxidase (AuNRs@PLL-GOx) composite hydrogel, which enables noninvasive, highly sensitive, and multimode detection of sweat glucose. The polylysine (PLL) interfacial layer provides abundant amino groups for glucose oxidase (GOx) immobilization, improves the dispersion of gold nanorods (AuNRs) within the hydrogel matrix, and facilitates interfacial charge transport by maintaining close contact between GOx and the conductive AuNRs network. These effects improve the electron-transfer efficiency and analytical performance of the hydrogel sensor. Furthermore, the platform integrates differential pulse voltammetry (DPV), cyclic voltammetry (CV), and chronoamperometry (i-t) within a single hydrogel system for sweat glucose monitoring. Benefiting from synergistic and multitechnique detection, the sensor demonstrated excellent analytical performance, including wide detection range (up to 160 μM), low detection limit (3.71 μM), and strong anti-interference capability. These results indicate that this hydrogel-based platform is well suited for future wearable biosensors and smart healthcare systems.
30 July 2026
Read appraisal →PloS one
The economic burden of Type 2 Diabetes by social determinants of health: A systematic review.
BACKGROUND: The unequal distribution of resources in society generates social gradients that translate into health inequalities and differential use of health care resources and their costs. Non-medical factors such as employment, income, ethnicity and education impact the prevalence and treatment outcomes of patients with type 2 diabetes mellitus (T2DM); however, there is a scarcity of articles assessing the relationship between health inequalities and the economic costs of treatment. Therefore, we conducted a systematic review of published studies examining the cost differences of treating T2DM across social determinants of health (SDH). METHODS: We systematically searched MEDLINE, Embase, PsycINFO, EconLit, and NHS EED for original peer-reviewed articles that provided cost differences of treating T2DM by SDH: education, income, employment, residency and ethnicity. We grouped the studies by each SDH and calculated the percentage differences where possible between the lowest and highest ends of the gradient (education, income and employment). Residency was categorised as rural vs. urban and ethnicity as white or general population vs other ethnic minorities. RESULTS: We included 19 articles retrieved internationally from varying healthcare systems. Results were contextualised given the healthcare financing model. In countries with high out-of-pocket expenses, Black and Hispanic ethnic backgrounds and rural residence were associated with lower direct health care and costs likely to be determined by ability to pay rather than clinical need. Indirect costs such as lost productivity due to absenteeism were also lower in unemployed, and lower income groups. CONCLUSIONS: There are evident health disparities in the direct and indirect economic consequences of T2DM. The effect of decreased healthcare use and costs on treatment outcomes needs to be further explored to inform policies to ensure healthcare delivery is based on clinical need rather than socio-economic factors.
28 July 2026
Read appraisal →PloS one
Artificial intelligence for early detection of diabetic retinopathy: A vision transformer-based approach
BACKGROUND: Early identification of diabetic retinopathy (DR), which is a primary cause of vision impairment globally, is a crucial phasis for effective intervention and treatment. Traditional screening workflows rely on manual diagnosis by ophthalmologists, which remains the gold standard but can be time-consuming and subject to variability due to human factors. To support and enhance the screening process, artificial intelligence (AI)-based tools have shown promise in automating DR detection, particularly with recent advances in deep learning. However, medical images with long-range dependencies and spatial linkages can be challenging for CNN-based algorithms to handle. METHODS: This paper proposes a Vision Transformer (ViT)-based model, specifically using a Compact Convolutional Transformer (CCT), for early automated detection of DR. The model uses self-attention techniques to improve feature extraction and classification performance; combining three main stages: the CCT tokenizer, transformer encoder, and sequence pooling. The proposed approach was trained on public datasets (EyePACS and APTOS 2019) and evaluated against state-of-the-art deep learning architectures. RESULTS: Our experimental findings demonstrate that ViT performs among the best in the current state of the art with an overall accuracy of 97% and F1-scores above 0.95 across all DR severity levels. Our system is primarily designed for the pre-screening stage of diabetic retinopathy workflows, enabling rapid and reliable identification of potential DR cases for further clinical evaluation. CONCLUSION: These results highlight the potential of transformer-based designs in medical picture analysis, as well as the implications for telemedicine and e-health solutions in real-time, especially in cases of low-resource settings.
28 July 2026
Read appraisal →Medicine
A bibliometric analysis of global trends in AI-driven digital health technologies for diabetes management
BACKGROUND: Digital health technologies are increasingly applied in diabetes care, enabling continuous monitoring, personalized support and remote interventions. Meanwhile, artificial intelligence (AI) is enhancing the precision and effectiveness of these tools. This study aims to map global research trends and thematic developments in AI-driven digital health technologies for diabetes management and to explore their future directions. METHODS: We collected data from the Web of Science Core Collection, including articles and reviews published up to July 12, 2025, using CiteSpace, VOSviewer, and Microsoft Excel to analyze countries/regions, institutions, journals, references, authors, and keywords. RESULTS: A total of 673 publications were included in the analysis. Global publications on AI-driven digital health technologies for diabetes increased steadily, with the USA leading in output. The University of London ranked as the most productive institution. Sensors and diabetes care were the most frequently published and cited journals in this field. Herrero P was among the most prolific authors. The most cited article was "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs." "diabetes" was the most frequently occurring keyword. Keyword cluster analysis identified 3 primary research hotspots: AI-enabled monitoring, digital health interventions, and AI-based diabetic retinopathy screening. CONCLUSIONS: This study summarizes the evolution of AI-driven digital health technologies in diabetes care. Although challenges remain in data security, standardization and validation, these technologies hold increasing potential for accurate diagnosis, real-time monitoring and personalized care.
26 July 2026
Read appraisal →Heart (British Cardiac Society)
Impact of diabetes on outcomes in hypertrophic cardiomyopathy: a GRADE meta-analysis
BACKGROUND: Diabetes mellitus (DM) is a common comorbidity in hypertrophic cardiomyopathy (HCM) and may exacerbate arrhythmic risk, promote structural remodelling and worsen heart failure outcomes. Its overall prognostic impact and effect on cardiac structure and function in adults with HCM remain uncertain. METHOD: We systematically searched PubMed, Scopus, Web of Science and Cochrane to January 2025 for observational studies comparing adults with HCM-DM versus HCM without DM. Random-effects meta-analyses were performed to pool ORs for clinical outcomes and standardised mean differences (SMDs) for echocardiographic parameters. Certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework after Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) evaluation. Subgroup, sensitivity and heterogeneity analyses were undertaken. RESULT: Eight studies encompassing approximately 47 592 patients met inclusion criteria. DM was associated with higher odds of all-cause mortality (OR 1.43, 95% CI 1.29 to 1.58; high certainty), heart failure (OR 1.34, 95% CI 1.25 to 1.43; moderate certainty) and atrial fibrillation (OR 1.41, 95% CI 1.18 to 1.68; high certainty). The association with atrial fibrillation was most pronounced in patients younger than 50 years (OR 2.55) and attenuated in those with body mass index ≥30 kg/m². HCM-DM was also linked to smaller left ventricular end-diastolic volumes (SMD -0.26) and impaired global longitudinal strain (SMD 0.58), consistent with subclinical systolic dysfunction, although heterogeneity was high and certainty low to moderate. Evidence for left ventricular ejection fraction, mass and septal thickness was inconclusive. Results were robust across sensitivity analyses. CONCLUSIONS: DM is a clinically important risk marker in HCM, associated with excess mortality, heart failure and atrial fibrillation, as well as adverse structural-functional changes. These findings support closer rhythm and function monitoring in HCM-DM and highlight the need for prospective studies to determine whether targeted metabolic interventions can improve outcomes. PROSPERO REGISTRATION NUMBER: CRD420250650799.
26 July 2026
Read appraisal →Journal of materials chemistry. B
Multimodal health monitoring and theranostics based on functionalized hydrogels and artificial intelligence
Functionalized hydrogels are ideal flexible interfaces for multimodal health monitoring and integrated diagnosis-therapy systems, owing to their tissue-like mechanical properties, programmable biochemical functions, and hierarchical pores. However, practical applications are often limited by several material bottlenecks: mechanical fatigue and conductivity loss under cyclic stress, the mismatch between degradation rate and functional lifespan, and the trade-off between sensitivity and biocompatibility. To address these challenges, artificial intelligence (AI) has been applied to accelerate structural optimization and property prediction through molecular network engineering and inverse design. Meanwhile, during the collection of coupled mechanical and biochemical signals, these interfaces usually suffer from high background noise, data variability, and baseline drift. Machine learning and deep learning can process these complex datasets through noise filtering, automated feature extraction, and pattern recognition, enabling continuous monitoring and adaptive health management. This review summarizes the recent material design strategies of functionalized hydrogels, AI-driven data analysis methods, and their progress and challenges in integrated diagnosis and therapy.
23 July 2026
Read appraisal →Inflammation research : official journal of the European Histamine Research Society ... [et al.]
Lipopolysaccharide-mediated macrophage polarization, conserved pathogenesis, and implications for peripheral neuropathy: a systematic review.
OBJECTIVE AND DESIGN: This systematic review synthesized evidence for a conserved lipopolysaccharide (LPS)-mediated pathogenic mechanism across diverse tissues and evaluated its potential relevance to peripheral neuropathy. METHODS: Studies were identified in which LPS was the independent exposure and pro-inflammatory, M1-like macrophage activation/polarization was an outcome. Structured evidence mapping was used to code in-vivo studies for direct measurement of prespecified steps along a proposed pathway: gut perturbation→barrier disruption→circulating LPS→systemic inflammation→tissue interface disruption→innate immune activation→M1-like macrophage skew→tissue dysfunction. Conditional concordance and downstream chain completeness scores were calculated. RESULTS: Mechanistic patterns were conserved between pulmonary, cardiac, renal, lymphatic, gastrointestinal, central nervous, adipose, osseous, urologic, dental, hepatic, uterine, and pancreatic tissues. Conditional concordance with the proposed pathway was high (mean 0.984 ± 0.053). Eleven studies assessed all downstream steps from LPS exposure to tissue dysfunction, each demonstrating full chain completeness. M1 macrophage skew (87%), innate immune activation (87%), and circulating LPS (82.6%) were the most frequently reported steps. CONCLUSIONS: These findings demonstrate conservation of LPS-driven M1-like macrophage polarization and tissue injury across systems, supporting the need to further investigate the biological plausibility of a gut-immune-nerve axis contributing to peripheral neuropathy.
18 July 2026
Read appraisal →Biosensors & bioelectronics
Functional wearable hydrogel microneedle platform for continuous ketone monitoring: Translating from rodents to humans.
Diabetic ketoacidosis (DKA) is a life-threatening complication of diabetes, driven by excessive ketone production; it is most common in type 1 diabetes. Continuous ketone monitoring (CKM) could enable earlier detection and prevention of DKA, yet no wearable CKM solution is clinically available. We present a wearable CKM patch that couples hydrogel microneedles (HMNs) for painless interstitial fluid (ISF) access with an enzymatic, electrochemical biosensor for on-patch detection of ketone bodies. The HMN patch and ketone biosensor were individually characterized and optimized in vitro, then integrated into a single platform. We validated the performance of the integrated CKM sensing patch across species: in vivo testing in healthy and diabetic rats, translation to a swine model, and a pilot evaluation in human participants. The CKM sensing patch reliably tracked dynamic ketone fluctuations in all models. To our knowledge, this is the first HMN-based biosensor tested in humans. These results establish a minimally invasive, wearable approach for continuous ketone tracking with the potential to transform outpatient DKA monitoring and early intervention.
16 July 2026
Read appraisal →Current diabetes reports
Digital Management of Early-Onset Type 2 Diabetes: Empowerment, Challenges, and Future Outlook
PURPOSE OF REVIEW: Early-onset type 2 diabetes (EOT2D), defined as a diabetes diagnosis before 40 years of age, is rising globally and associated with an aggressive disease course and early complications. This review examines the role of digital health technologies (DHT) in addressing the unique clinical and life-course challenges of EOT2D. RECENT FINDINGS: DHT, including continuous glucose monitoring, mobile health applications, digital therapeutics, telemedicine, remote patient monitoring, wearable devices, and artificial intelligence-based analytics, have demonstrated modest improvements in glycemic control, weight management, and patient engagement in people with type 2 diabetes. However, evidence in adults with EOT2D remains limited. Compared with usual-onset T2D, people with EOT2D may derive particular benefits due to higher digital literacy, greater lifestyle variability, and longer anticipated disease duration. Although DHT shows promise for improving empowerment and care integration in EOT2D, important gaps persist, including a lack of EOT2D-specific trials, digital divide-related inequities, interoperability challenges, and reimbursement barriers. Future research should prioritize tailored interventions and hybrid care models to optimize long-term outcomes in this high-risk population.
11 July 2026
Read appraisal →Journal of epidemiology and community health
Analysis of the potential association between physical activity and skin autofluorescence: a systematic review.
INTRODUCTION: Accumulation of advanced glycation end products, measured by skin autofluorescence (SAF), has been shown to be associated with several chronic non-communicable diseases, particularly cardiovascular diseases (CVDs). The promotion of physical activity (PA) as a strategy for the prevention of CVD by modifying healthy habits has been widely studied. AIM: To assess the evidence for the association between PA and SAF in the general adult population. METHODS: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Synthesis Without Meta-analysis framework. A search was performed in MEDLINE (via PubMed), Web of Science, Scopus, Cochrane Library and SportDiscuss (via EBSCOhost), from inception to September 2024. Study quality was assessed using the National Heart, Lung and Blood Institute tools and the certainty of evidence was evaluated with Grading of Recommendations, Assessment Development and Evaluation. Vote counting based on the direction of effect was used as the standardised synthesis metric. RESULTS: In the systematic review, 17 studies were included. The qualitative synthesis showed a predominant consistency in favour of a beneficial association. Specifically, 58.8% of the studies reported a statistically significant inverse association, indicating that higher levels of PA or exercise frequency are correlated with lower SAF levels. The remaining studies (41.2%) reported non-significant results, though several showed favourable trends. No studies reported a positive association between PA and SAF. The quality of the studies was generally fair, and the certainty of evidence low. CONCLUSIONS: PA is inversely associated with SAF. Therefore, while causality cannot be proven, it is hypothesised that PA may reduce SAF and thus have a positive impact on health.
11 July 2026
Read appraisal →International ophthalmology
The relationship between diabetic retinopathy and intestinal microbiota: a systematic review and meta-analysis
PURPOSE: The results of human observational studies on the correlation between gut microbiota and diabetic retinopathy (DR) are discrepant. This meta-analysis aimed to evaluate the specificity of the gut microbiota in DR patients compared to patients with type 2 diabetes mellitus (T2DM). METHODS: All published literature up to October 2024 was searched by two researchers on PubMed, Embase and Web of Science databases. Diversity and gut microbiota composition were the main outcomes. The meta-analysis was conducted in Review Manager (RevMan) Version 5.3. RESULTS: Eight studies, investigating gut microbiota by collecting stool samples, conducted in China and India were included in this meta-analysis, involving a total of 486 individuals in the T2DM (n = 258) and DR (n = 228) groups. No significant difference in alpha-diversity was observed between T2DM and DR patients. The pooled estimate showed that, at the phylum level, the abundances of Patescibacteria, and Synergistetes were significantly lower, and Verrucomicrobia were considerably higher in DR patients than in T2DM patients. At the genus level, DR patients had an increase in Bacteroides compared to T2DM. CONCLUSIONS: In this meta-analysis with a small number of studies and relatively high heterogeneity, changes in gut microbiota were associated with DR, commonly reflected by a reduction in beneficial species and an increase in pathogenic species influencing metabolic pathways.
10 July 2026
Read appraisal →Food & function
Apples and apple-based products in the modulation of cardiometabolic and functional markers: a systematic review of human intervention studies.
Apple is one of the most widely consumed fruits worldwide, yet its effects on human health remain the subject of ongoing debate. The aim of the present review was to examine evidence from human intervention studies evaluating the impact of apples and apple-based products on functional and metabolic health markers. A total of 38 studies were included: 13 postprandial interventions, 22 medium- or long-term interventions, and 3 assessing both postprandial and chronic effects. Postprandial studies predominantly investigated the effects of apple consumption on blood glucose levels and plasma antioxidant capacity, whereas medium- or long-term interventions assessed a broader range of biomarkers related to cardiometabolic health, oxidative stress, vascular function, inflammation, and gut function. Overall, the findings suggest that apples and apple-based products may beneficially modulate glycaemia, antioxidant capacity, and vascular endothelial function mainly in short-term interventions, while medium/long-term studies reported an apparent improvement in gut microbiota composition. However, the current evidence remains insufficient to draw definitive conclusions. Additionally, substantial heterogeneity in study design, populations, and products tested limits the ability to generalize results. Nonetheless, apple consumption, consistent with fruit intake in general, represents an important component of a healthy and balanced diet, providing valuable nutrients and bioactive compounds whose intake should be encouraged. Therefore, further well-designed intervention studies, particularly in populations with cardiometabolic risk factors, are warranted to better clarify the role of apples and apple-derived products in human health. Future research should also aim to identify the effective amounts and specific bioactive components responsible for the observed effects, as well as to determine whether these benefits may vary according to the health status of the target population.
6 July 2026
Read appraisal →The Nigerian postgraduate medical journal
The Role of Digital Therapeutics and Artificial Intelligence in Chronic Disease Management: A Narrative Review
We conducted research showing that chronic disease management continued to challenge healthcare systems, payers, and patients. At the same time, digital therapeutics emerged as a promising and potentially transformative approach. They supported better management of long-term conditions, improved patient outcomes, and helped streamline healthcare delivery. We carried out a comprehensive literature search to identify both original reports and reviewed publications. The search covered multiple databases, including Google Scholar and PubMed, and we also gathered relevant information from credible online sources such as the World Health Organization and India's National Crime Records Bureau. Using these findings, the narrative explained how digital therapeutics reshaped chronic disease management by highlighting key benefits, the technologies that enabled these solutions, and the expected impacts on both clinical and economic outcomes. We also discussed the obstacles the sector encountered as it developed, and we considered the future prospects of digital therapeutics in chronic care. In conclusion, digital therapeutics offered personalised care, improved patient engagement and adherence, provided real-time monitoring and feedback, enhanced accessibility and convenience, and were cost-effective in managing chronic disease; however, challenges such as cybersecurity concerns, reliability of data, the digital divide, and a lack of extensive clinical validation needed to be addressed for widespread adoption. While the evidence to date suggested clear clinical and economic promise, realizing that promise required coordinated action stronger clinical trials to build robust evidence, clear regulatory pathways to ensure safety and efficacy, investment in secure interoperable infrastructure, and targeted efforts to close the digital divide so vulnerable populations were not left behind; policymakers, clinicians, payers, and technology developers had to collaborate to translate innovation into equitable, scalable care improvements.
3 July 2026
Read appraisal →Experimental eye research
Artificial intelligence applications in OCT and OCTA for diabetic retinopathy: A systematic review
PURPOSE: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide. Optical coherence tomography (OCT) and OCT angiography (OCTA) provide detailed retinal imaging, enabling early detection of microvascular changes. This study aims to systematically review artificial intelligence (AI), particularly deep learning (DL), applications for DR detection and analysis using OCT and OCTA images. METHODS: A comprehensive literature search was conducted across PubMed, Web of Science, Scopus, IEEE Xplore, and Embase for studies published up to March 2026. A total of 1007 articles were identified, of which 175 studies met the inclusion criteria following the PRISMA study selection process. RESULTS: DL-based approaches consistently demonstrated superior performance compared to traditional machine learning (ML) methods, with reported AUC values typically ranging from 0.90 to 0.99 across classification and segmentation tasks. Convolutional neural networks (CNNs), Vision Transformers (ViTs), and encoder-decoder architectures such as U-Net showed strong performance in detecting key DR biomarkers, including microaneurysms, macular edema, and neovascularization. However, performance variability was observed depending on dataset size, imaging modality, and annotation quality. CONCLUSIONS: AI-driven analysis of OCT and OCTA images offers significant potential for automated DR detection. Despite promising results, challenges such as limited public datasets, lack of cross-institutional validation, and model interpretability remain. Future research should focus on multimodal integration, explainable AI, and large-scale validation to enhance clinical applicability.
2 July 2026
Read appraisal →Journal of diabetes science and technology
The Impact of Virtual Consultations on Quality of Care for Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis
BackgroundVirtual consultations (VC) have transformed healthcare delivery, offering a convenient and effective way to manage chronic conditions such as Type 2 Diabetes (T2D). This systematic review and meta-analysis evaluated the impact of VC on the quality of care provided to patients with T2D, mapping it across the six domains of the US National Academy of Medicine (NAM) quality-of-care framework (ie, effectiveness, efficiency, patient-centeredness, timeliness, safety, and equity).MethodsA systematic search was conducted in PubMed/MEDLINE, Cochrane, Embase, CINAHL, and Web of Science for the period between January 2010 and December 2024. Eligible studies involved adult T2D patients, evaluated synchronous VCs, and reported outcomes relevant to NAM quality domains. Two independent reviewers performed screening, and studies were assessed using the Mixed Methods Appraisal Tool (MMAT). A narrative synthesis was conducted for each quality domain, and a meta-analysis of HbA1c levels was performed using random-effects models.ResultsIn total, 15 studies involving 821 014 participants were included. VCs were comparable with face-to-face care in effectiveness, efficiency, patient-centeredness, and timeliness, with improvements in accessibility and patient satisfaction. Mixed results were found for safety due to limitations in physical assessments, and for equity, with older adults and those with lower digital literacy facing more challenges. The meta-analysis showed no significant difference in HbA1c reduction between VCs and face-to-face (standardized mean difference [SMD] = -0.31, 95% confidence interval [CI]: -0.71 to 0.09, P = 0.12).ConclusionVCs offer a promising alternative to in-person care, but addressing digital disparities and improving access for older adults are essential for maximizing VC potential.
2 July 2026
Read appraisal →Physiology (Bethesda, Md.)
Wearable Sensing for Clinical Physiology Monitoring: Emerging Paradigms
Advances in wearable sensing technology are driving a new era of personalized health monitoring. In contrast to the hard, rigid form factors of conventional wearable sensors, these emerging skin-interfaced systems support high-quality physiological measurements across biophysical, biochemical, and kinematic signals of interest. These platforms enable continuous monitoring of complex physiological processes with unprecedented detail as a result of a seamless, conformal skin interface and advanced wireless communications capabilities. These platforms integrate flexible materials, miniaturized electronics, and wireless communication to provide detailed physiological data during daily activities. This review examines how skin-interfaced wearables are advancing patient care, remote monitoring, and large-scale health studies. We highlight critical barriers to clinical adoption including interpreting data, validating devices, and integration into health care systems. Key opportunities include sustainable manufacturing, point-of-care fabrication, and development of disease-specific digital biomarkers. By addressing these challenges through collaboration among engineers, clinicians, and data scientists, wearable sensors can expand patient access to advanced physiological monitoring and transform personalized medicine.
2 July 2026
Read appraisal →Advances in skin & wound care
Mobile Health App Needs Among Patients With Diabetic Foot Ulcers in China: A Qualitative Study From the Perceptions of Patients, Caregivers, and Health Care Professionals
OBJECTIVE: To explore the specific needs of patients with diabetic foot ulcers (DFUs), caregivers, and health care professionals (HCPs) for a mobile health (mHealth) app, aiming to inform the design and development of effective mHealth service solutions. METHODS: This descriptive qualitative study was conducted from June to September 2024 in the wound care clinics of 2 local hospitals. Participants included patients with DFUs, caregivers, and HCPs directly involved in their care. Interview data were analyzed, synthesized, and refined using content analysis. RESULTS: Five key themes emerged: the pressing need to implement mHealth app services, convenient and personalized access to information, continuous and specialized health guidance, a multidisciplinary approach to disease management, and free access alongside privacy and legal protections. CONCLUSIONS: This study provides valuable insights for the design and development of an mHealth app for DFU. During the development process, it is essential to consider user needs and ensure the app meets the expectations of patients and related groups for personalized, continuous, and specialized health guidance; free access; privacy protection; and multidisciplinary collaboration. A balance should be struck between convenience and security of the app's features, to encourage user engagement and enhance the app's value and effectiveness.
2 July 2026
Read appraisal →Journal of medical Internet research
Operationalizing Digital Health Equity in Artificial Intelligence-Enabled Patient Decision Aids for Older Adults: Mixed Methods Study
BACKGROUND: Artificial intelligence-enabled patient decision aids (AI-PDAs) hold promise for supporting older adults with chronic diseases in accessing personalized health information, clarifying preferences, and engaging in shared decision-making. Achieving equity in their design requires attention to the complex health care and digital contexts in which these tools are used. While the Digital Health Equity Framework (DHEF) provides a conceptual foundation, practical strategies for its application remain limited. OBJECTIVE: This study aimed to identify equity-related determinants and generate actionable design strategies for applying the DHEF to AI-PDAs for older adults. METHODS: A mixed methods study was conducted. Semistructured interviews were conducted with older adults living with hypertension and/or diabetes, health care providers, and medical students to explore equity determinants relevant to AI-PDAs. In parallel, a review of reviews synthesized existing evidence on approaches to addressing these determinants. Interview findings and review findings were integrated through an iterative mapping process conducted by the research team and refined through multidisciplinary expert consultation involving medicine, public health, social services, and computer science. RESULTS: A total of 33 stakeholders were interviewed, including 15 older adults, 8 health care providers, and 10 medical students. Thirteen reviews were included in the umbrella review. The integrated synthesis identified equity determinants spanning individual, interpersonal, community, and societal levels across both the health care and digital environments, together with cross-level concerns related to algorithmic fairness. These findings informed 5 recommendations for equitable AI-PDA development: (1) co-design with end users to address their needs, (2) embrace relationship-centered design, (3) leverage community resources to improve support, (4) promote accessible and equitable artificial intelligence (AI) governance in society, and (5) enhance equitable AI through algorithmic fairness. Together, these recommendations provide practical guidance for design, pilot testing, implementation, and evaluation. CONCLUSIONS: By integrating stakeholder perspectives with synthesized review evidence, this study extends the DHEF from a primarily conceptual framework toward a more practice-oriented approach for AI-PDAs for older adults with chronic disease. Health care settings serve as a mediating sociotechnical context where AI tools may either support or constrain equitable care participation. The findings underscore the need for interdisciplinary collaboration to align technological innovation with equity-oriented design. Future work should focus on co-designed prototypes, real-world testing, and measurable equity outcomes.
30 June 2026
Read appraisal →ACS sensors
Flexible Wearable Closed-Loop Diagnostic and Therapeutic System: A New Paradigm for Skin Health Management
As the largest external barrier of the human body, the skin is essential to overall health and quality of life. Conventional skin health management faces limitations such as insufficient real-time monitoring and lack of personalization. Flexible wearable devices have recently emerged as a transformative technology, enabling non-invasive sensing and targeted therapy with high skin compatibility. This review comprehensively summarizes recent advances in flexible wearable closed-loop diagnostic and therapeutic systems for skin health management. It outlines the core technological components, including skin bioinformation collection, biosignal monitoring, signal transmission and data analysis, and targeted intervention. Key applications in physiological and pathological monitoring, wound healing, drug delivery, and physical therapy are discussed. Furthermore, the innovation trajectory of closed-loop systems is clarified, key challenges are analyzed, and targeted solutions are proposed. Future directions are prospected from cross-disciplinary views including digital twins and personalized medicine, establishing a new paradigm for real-time, personalized, and continuous skin health management.
28 June 2026
Read appraisal →Placenta
Deep learning-based early prediction of gestational diabetes mellitus through first-trimester placental texture analysis
INTRODUCTION: This study aimed to develop a multi-parameter fusion model for early GDM risk prediction and validate its performance through external multicenter testing. METHODS: A total of 628 pregnant women at 11+0-13+6 weeks were enrolled from two medical centers. The Center I cohort was divided into training (n = 356) and testing sets (n = 153). Radiomic features (1,289) and deep learning features (2,048) were extracted from placental ultrasound images. Feature-level fusion resulted in 3337 features, which were selected using Spearman correlation, mRMR, and LASSO. Five models were built: Rad Model, DTL Model, DLR Model, Clinic Model, and Combined Model. Performance was assessed using ROC analysis, DCA, and calibration curves. RESULTS: The Combined Model achieved the best overall performance, with an area under the ROC curve (AUC) of 0.879 in the internal validation, significantly outperforming any single-modality model (P < 0.05). DCA demonstrated that the fusion-based model provided higher net clinical benefit across a wide range of threshold probabilities compared with both "treat-all" and "treat-none" strategies. The calibration curve showed excellent agreement between predicted and observed probabilities (Hosmer-Lemeshow test, P > 0.05). DISCUSSION: The multimodal fusion model enhanced early GDM prediction by detecting subtle placental changes in first-trimester, enabling timely intervention and personalized decision-making.
27 June 2026
Read appraisal →Journal of health organization and management
Unlocking potential: impact of mHealth affordance realization on patient well-being
PURPOSE: This study investigates how realized affordances of mobile health technologies influence the relationship between technology use, patient empowerment and well-being in diabetes self-management. DESIGN/METHODOLOGY/APPROACH: The research proposes a technology use-empowerment-well-being (TEW) model based on affordance theory. Data were collected through a survey of 257 diabetes patients using mobile technologies for self-management and analyzed using structural equation modeling with SmartPLS. FINDINGS: Results indicate that patient empowerment fully mediates the relationship between mobile technology use and patient well-being. Importantly, realized affordances moderately strengthen the relationship between technology use and patient empowerment, demonstrating that patients derive greater benefits when they actively utilize technology's capabilities for diabetes management. RESEARCH LIMITATIONS/IMPLICATIONS: Reliance on self-reported data and the cross-sectional design limit causal inferences about the evolution of technology-empowerment-well-being relationships over time. PRACTICAL IMPLICATIONS: Healthcare technology designers should focus on creating clear, tangible benefits for patients that support specific self-management behaviors. Healthcare providers should educate patients about the benefits of technology and support them in realizing these affordances through training and ongoing assistance. ORIGINALITY/VALUE: This study integrates affordance theory with patient empowerment concepts, providing empirical evidence that realized affordances - not merely technology availability - are critical for improving self-management outcomes. The TEW model introduces a novel framework for understanding how technology empowers patients and enhances well-being in chronic disease management.
27 June 2026
Read appraisal →Journal of medical Internet research
Extracting and Classifying Drug Discontinuations From Estonian Electronic Health Records: Development and Validation Study
BACKGROUND: Drug adherence is crucial for chronic disease management, yet treatment discontinuation remains common due to factors such as side effects, inefficacy, or cost. These reasons are often recorded only in free-text clinical notes, making large-scale analysis difficult. While large language models (LLMs) can interpret such unstructured data more effectively than traditional natural language processing methods, few studies have systematically categorized reasons for discontinuation or identified whether the decision was initiated by the patient or the clinician, especially in low-resource languages such as Estonian. OBJECTIVE: This study aimed to assess the ability of LLMs to extract and classify reasons for drug discontinuation and identify who initiated it using Estonian electronic health records and characterize the observed discontinuation patterns and initiators for statins and antidiabetic medications. METHODS: We combined prescription data with free-text anamneses from a 10% sample of the Estonian population (2012-2019). LLMs (Llama 3.1-70B and GPT-4o) were applied to extract discontinuation phrases and reasons, classify them into a clinician-developed taxonomy, and identify who discontinued the treatment. Performance was evaluated on 100 randomly chosen cases per drug group. RESULTS: Extraction yielded 625 antidiabetic drug and 233 statin discontinuation cases. Validation confirmed a precision of 0.93 to 0.98 for extracting phrases and 0.95 to 0.96 for extracting reasons. Classification of discontinuation reasons achieved weighted F1-scores of 0.81 to 0.84, whereas classification of who initiated discontinuation achieved weighted F1-scores of 0.64 to 0.78. Adverse reactions were the most frequent reason overall, accounting for 70% (163/233) of statin discontinuations and 44.8% (280/625) of antidiabetic drug discontinuations. Regarding antidiabetic drugs, treatment inefficacy and contraindications were more common. Patients more often stopped due to adverse reactions or nonmedical reasons, whereas physicians more often initiated discontinuation for contraindications. CONCLUSIONS: LLMs can accurately extract and classify medication discontinuation reasons and show variable performance in identifying discontinuation initiators in Estonian clinical narratives. Both local and proprietary models showed promising results, enabling scalable analyses that complement structured health records. This demonstrates the potential of LLMs to unlock information from clinical notes, turning this underused electronic health record component into a valuable resource for monitoring treatment patterns and detecting adverse event signals.
19 June 2026
Read appraisal →